Many students have a difficult time understanding standardization when starting out in learning statistics. Common questions often include:
The answers are fairly straightforward. Here’s a rundown for your statistical woes.
Welcome to another stupidly-long, but hopefully informative instructional on introductory statistical concepts. Today we tackle regression analysis. Use the menu links below to jump around if you need/want to get a quick bit of info on any topic:
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1) Background: Correlation analysis [conceptually] explained
2) Correlation analysis and OLS linear regression
3) From guesses to predictions: The logic of using linear regression
— a) Building the equation
— b) Interpreting regression
— c) What is OLS?
4) Advanced Applications
5) Conclusion & Further reading
Today’s tutorial involves picking up a useful new weapon for your data analytic arsenal; one that I’ve used quite a bit over the past year of my graduate training. We’re going to look at a novel way of estimating & graphing interactions in the context of multiple regression (one that even extends to structural equation models), using my increasingly go-to program – Mplus. Note that the tips below have been tested in Mplus versions 6 and 7 effectively. Using these procedures in any earlier version is a total crap shoot — meaning I haven’t verified whether or not they work in version 5 or older — so bear that in mind.
Every field has its landmark papers. You know – the real game changers. In our field, these are often synonymous with those papers that make their way inexorably into almost every single social psychology lecture across the globe (e.g., Milgram, 1963). The papers I’ve listed here are perhaps not quite in that league [yet!]. However, they are papers that I’ve come across in my travels through grad school, to which I’ve found myself returning again and again. Continue reading
Back again with a new Excel tool. This one, which I’ve titled the “Coefficient Cruncher” is a recent development that I’ve been using to write up various sets of results, and it has greatly accelerated my output rate. One of the most tedious things about writing up results is… well, writing up results. This helps.
WITH statement outputs).UPDATE: The newer, better version 2.1 is now out. Click here for more info.
The Model Fit Aggregator is a tool I designed for use with Mplus. It allows you to use the model fit information from any model you estimate via maximum likelihood estimation and plug it in (where instructed). The tool will aggregate the information from the raw output and spit out a single line of model fit statistics for you to paste into a manuscript, poster, talk, or other document. Continue reading
The Correlation Tabulator is a tool I designed for use with SPSS. It will require you to run a set of Pearson correlations in SPSS, paste the correlation table output into the tabulator (if you follow the instructions, of course). It will then take those results and compile them into an APA-style correlation table (coefficients reported to two decimal places, with asterisks indicating significance levels), which you can copy and paste into Word or a similar program.
The Regression Tabulator is an Excel-based tool developed for use with SPSS regression analysis output. It is designed to accommodate multiple regression with a maximum of 20 predictor variables (which you will need to define). If you paste your “Coefficients” table into the worksheet (with or without confidence intervals), it will convert your SPSS output into three APA-style tables for you to choose from. The first features complete information unstandardized coefficients (B, SE, t, p). The second and third are truncated tables (unstandardized and standardized, respectively) that include reports of the model coefficients and standard errors, with asterisks indicating significance levels.
Fred Clavel, Ph.D. 